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Crisis management, destination recovery and sustainability: Tourism at a crossroads
The COVID-19 pandemic brought travel to a halt and the global tourism industry has been one of the sectors hit hardest during the pandemic. This book looks at how the tourism industry can enhance its resilience and prepare for future crises more effectively. The book provides insights into the economic, social, geopolitical and environmental implications of the COVID-19 pandemic on the tourism and hospitality industries and the responses in diverse international contexts. It highlights key concepts and includes cases with real-life applications. The book also discusses future research directions in a post-pandemic scenario. This book will be an invaluable resource for practitioners in the areas of tourism and crisis management and for readers to compare and contrast tourism destination recovery and crisis management practices through different research methodologies and settings. 2023 selection and editorial matter, James Kennell, Priyakrushna Mohanty, Anukrati Sharma and Azizul Hassan. All rights reserved. -
Cyber-Physical Systems: AI and COVID-19
Cyber-Physical Systems: AI and COVID-19 highlights original research which addresses current data challenges in terms of the development of mathematical models, cyber-physical systems-based tools and techniques, and the design and development of algorithmic solutions, etc. It reviews the technical concepts of gathering, processing and analyzing data from cyber-physical systems (CPS) and reviews tools and techniques that can be used. This book will act as a resource to guide COVID researchers as they move forward with clinical and epidemiological studies on this outbreak, including the technical concepts of gathering, processing and analyzing data from cyber-physical systems (CPS). The major problem in the identification of COVID-19 is detection and diagnosis due to non-availability of medicine. In this situation, only one method, Reverse Transcription Polymerase Chain Reaction (RT-PCR) has been widely adopted and used for diagnosis. With the evolution of COVID-19, the global research community has implemented many machine learning and deep learning-based approaches with incremental datasets. However, finding more accurate identification and prediction methods are crucial at this juncture. 2022 Elsevier Inc. All rights reserved. -
Data Analytics for Social Microblogging Platforms
Data Analysis for Social Microblogging Platforms explores the nature of microblog datasets, also covering the larger field which focuses on information, data and knowledge in the context of natural language processing. The book investigates a range of significant computational techniques which enable data and computer scientists to recognize patterns in these vast datasets, including machine learning, data mining algorithms, rough set and fuzzy set theory, evolutionary computations, combinatorial pattern matching, clustering, summarization and classification. Chapters focus on basic online micro blogging data analysis research methodologies, community detection, summarization application development, performance evaluation and their applications in big data. 2023 Elsevier Inc. All rights reserved. -
Data Engineering and Data Science: Concepts and Applications
DATA ENGINEERING and DATA SCIENCE Written and edited by one of the most prolific and well-known experts in the field and his team, this exciting new volume is the one-stop shop for the concepts and applications of data science and engineering for data scientists across many industries. The field of data science is incredibly broad, encompassing everything from cleaning data to deploying predictive models. However, it is rare for any single data scientist to be working across the spectrum day to day. Data scientists usually focus on a few areas and are complemented by a team of other scientists and analysts. Data engineering is also a broad field, but any individual data engineer doesnt need to know the whole spectrum of skills. Data engineering is the aspect of data science that focuses on practical applications of data collection and analysis. For all the work that data scientists do to answer questions using large sets of information, there have to be mechanisms for collecting and validating that information. In this exciting new volume, the team of editors and contributors sketch the broad outlines of data engineering, then walk through more specific descriptions that illustrate specific data engineering roles. Data-driven discovery is revolutionizing the modeling, prediction, and control of complex systems. This book brings together machine learning, engineering mathematics, and mathematical physics to integrate modeling and control of dynamical systems with modern methods in data science. It highlights many of the recent advances in scientific computing that enable data-driven methods to be applied to a diverse range of complex systems, such as turbulence, the brain, climate, epidemiology, finance, robotics, and autonomy. Whether for the veteran engineer or scientist working in the field or laboratory, or the student or academic, this is a must-have for any library. 2023 Scrivener Publishing LLC. -
Data Science in the Medical Field
Data science has the potential to influence and improve fundamental services such as the healthcare sector. This book recognizes this fact by analyzing the potential uses of data science in healthcare. Every human body produces 2 TB of data each day. This information covers brain activity, stress level, heart rate, blood sugar level, and many other things. More sophisticated technology, such as data science, allows clinicians and researchers to handle such a massive volume of data to track the health of patients. The book focuses on the potential and the tools of data science to identify the signs of illness at an extremely early stage. 2025 Elsevier Inc. All rights are reserved including those for text and data mining AI training and similar technologies. -
Data-Driven Decision Making
This book delves into contemporary business analytics techniques across sectors for critical decision-making. It combines data, mathematical and statistical models, and information technology to present alternatives for decision evaluation. Offering systematic mechanisms, it explores business contexts, factors, and relationships to foster competitiveness. Beyond managerial perspectives, it includes contributions from professionals, academics, and scholars worldwide, delivering comprehensive knowledge and skills through diverse viewpoints, cases, and applications of analytical tools. As an international business science reference, it targets professionals, academics, researchers, doctoral scholars, postgraduate students, and research organizations seeking a nuanced understanding of modern business analytics. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Deep Learning for Sustainable Agriculture
The evolution of deep learning models, combined with with advances in the Internet of Things and sensor technology, has gained more importance for weather forecasting, plant disease detection, underground water detection, soil quality, crop condition monitoring, and many other issues in the field of agriculture. agriculture. Deep Learning for Sustainable Agriculture discusses topics such as the impactful role of deep learning during the analysis of sustainable agriculture data and how deep learning can help farmers make better decisions. It also considers the latest deep learning techniques for effective agriculture data management, as well as the standards established by international organizations in related fields. The book provides advanced students and professionals in agricultural science and engineering, geography, and geospatial technology science with an in-depth explanation of the relationship between agricultural inference and the decision-support amenities offered by an advanced mathematical evolutionary algorithm. 2022 Elsevier Inc. All rights reserved. -
Deep Learning for Sustainable Agriculture
The evolution of deep learning models, combined with with advances in the Internet of Things and sensor technology, has gained more importance for weather forecasting, plant disease detection, underground water detection, soil quality, crop condition monitoring, and many other issues in the field of agriculture. agriculture. Deep Learning for Sustainable Agriculture discusses topics such as the impactful role of deep learning during the analysis of sustainable agriculture data and how deep learning can help farmers make better decisions. It also considers the latest deep learning techniques for effective agriculture data management, as well as the standards established by international organizations in related fields. The book provides advanced students and professionals in agricultural science and engineering, geography, and geospatial technology science with an in-depth explanation of the relationship between agricultural inference and the decision-support amenities offered by an advanced mathematical evolutionary algorithm. 2022 Elsevier Inc. All rights reserved. -
Deep learning: Research and applications
This book focuses on the fundamentals of deep learning along with reporting on the current state-of-art research on deep learning. In addition, it provides an insight of deep neural networks in action with illustrative coding examples. Deep learning is a new area of machine learning research which has been introduced with the objective of moving ML closer to one of its original goals, i.e. artificial intelligence. Deep learning was developed as an ML approach to deal with complex input-output mappings. While traditional methods successfully solve problems where final value is a simple function of input data, deep learning techniques are able to capture composite relations between non-immediately related fields, for example between air pressure recordings and English words, millions of pixels and textual description, brand-related news and future stock prices and almost all real world problems. Deep learning is a class of nature inspired machine learning algorithms that uses a cascade of multiple layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input. The learning may be supervised (e.g. classification) and/or unsupervised (e.g. pattern analysis) manners. These algorithms learn multiple levels of representations that correspond to different levels of abstraction by resorting to some form of gradient descent for training via backpropagation. Layers that have been used in deep learning include hidden layers of an artificial neural network and sets of propositional formulas. They may also include latent variables organized layer-wise in deep generative models such as the nodes in deep belief networks and deep boltzmann machines. Deep learning is part of state-of-the-art systems in various disciplines, particularly computer vision, automatic speech recognition (ASR) and human action recognition. Tutorials on deep learning framework with focus on tensor flow, keras etc. Numerous worked out examples on real life applications Illustrative diagrams and coding examples. 2020 Walter de Gruyter GmbH, Berlin/Boston. All rights reserved. -
Deep Reinforcement Learning and Its Industrial Use Cases: AI for Real-World Applications
This book serves as a bridge connecting the theoretical foundations of DRL with practical, actionable insights for implementing these technologies in a variety of industrial contexts, making it a valuable resource for professionals and enthusiasts at the forefront of technological innovation. Deep Reinforcement Learning (DRL) represents one of the most dynamic and impactful areas of research and development in the field of artificial intelligence. Bridging the gap between decision-making theory and powerful deep learning models, DRL has evolved from academic curiosity to a cornerstone technology driving innovation across numerous industries. Its core premiseenabling machines to learn optimal actions within complex environments through trial and errorhas broad implications, from automating intricate decision processes to optimizing operations that were previously beyond the reach of traditional AI techniques. Deep Reinforcement Learning and Its Industrial Use Cases: AI for Real-World Applications is an essential guide for anyone eager to understand the nexus between cutting-edge artificial intelligence techniques and practical industrial applications. This book not only demystifies the complex theory behind deep reinforcement learning (DRL) but also provides a clear roadmap for implementing these advanced algorithms in a variety of industries to solve real-world problems. Through a careful blend of theoretical foundations, practical insights, and diverse case studies, the book offers a comprehensive look into how DRL is revolutionizing fields such as finance, healthcare, manufacturing, and more, by optimizing decisions in dynamic and uncertain environments. This book distills years of research and practical experience into accessible and actionable knowledge. Whether you're an AI professional seeking to expand your toolkit, a business leader aiming to leverage AI for competitive advantage, or a student or academic researching the latest in AI applications, this book provides valuable insights and guidance. Beyond just exploring the successes of DRL, it critically examines challenges, pitfalls, and ethical considerations, preparing readers to not only implement DRL solutions but to do so responsibly and effectively. Audience The book will be read by researchers, postgraduate students, and industry engineers in machine learning and artificial intelligence, as well as those in business and industry seeking to understand how DRL can be applied to solve complex industry-specific challenges and improve operational efficiency. 2024 Scrivener Publishing LLC. -
Digital technologies in modeling and management: Insights in education and industry
Digital Technologies in Modeling and Management: Insights in Education and Industry explores the use of digital technologies in the modeling and control of complex systems in various fields, such as social networks, education, technical systems, and their protection and security. The book consists of two parts, with the first part focusing on modeling complex systems using digital technologies, while the second part deals with the digitalization of economic processes and their management. The book results from research conducted by leading universities' teaching staff and contains the results of many years of scientific experiments and theoretical conclusions. The book is for a wide range of readers, including the teaching staff of higher educational institutions, graduate students, students in computer science and modeling, and management technologies, including economics. It is also a valuable resource for IT professionals and business analysts interested in using digital technologies to model and control complex systems. 2024 by IGI Global. All rights reserved. -
Education as a Determinant of E - Governance Adoption: A Cse Study of Tele Centres of Karnataka
Imperial Journal of Interdisciplinary Research, Vol. 2, Issue 13, pp. 32-38. -
Educational perspectives on digital technologies in modeling and management
The complexity of managing and modeling various systems, from social networks to educational institutions and technical systems, has reached unprecedented levels. Traditional approaches struggle to keep pace with the dynamic nature of these challenges, leaving scholars, educators, and professionals searching for innovative solutions. This growing problem calls for a comprehensive guide to harnessing the potential of digital technologies for effective modeling and management. Educational Perspectives on Digital Technologies in Modeling and Management is a groundbreaking book that addresses the pressing need for modern solutions to keep up with the evolving challenges of modeling systems. This research book emerges from the collaborative efforts of teaching staff from leading universities, and years of dedicated scientific research and experimentation. It focuses on modeling complex systems using digital technologies, presenting fresh insights and innovative methodologies for academics, scholars, and aspiring professionals who are delving into the world of digitalization of economic processes and their management. The text caters to the specific needs of educators, IT professionals, and business analysts. Covering a wide array of topics, from information security to speaker verification and turbulent gas flow, this book serves as a valuable resource for a diverse audience seeking to excel in their respective fields. Educational Perspectives on Digital Technologies in Modeling and Management is the solution to the contemporary challenges of modeling and managing complex systems. Whether you are a teaching staff member at a higher educational institution, a graduate student in computer science, or an IT professional striving to stay at the forefront of your field, this book is your indispensable guide to unlocking the power of digital technologies. 2024 by IGI Global. All rights reserved. All rights reserved. -
Effect of Impulse Buying on Socio-economic factors and Retail Categories
Indian Journal of Marketing, Vol. 46, Issue 9, pp. 24-34, ISSN No. 0973-8704 -
Effective and Meaningful Student Engagement Through Service Learning
A paradigm shift is underway in education, challenging traditional teaching methods and calling for a more engaging and purposeful approach. It is necessary to explore how service learning empowers students to address real-world issues, fostering critical thinking, creativity, collaboration, and communication skills essential for the 21st century. Effective and Meaningful Student Engagement Through Service Learning is a comprehensive exploration of the transformative power of service learning in contemporary education. Within this text, seasoned researchers and practitioners delve into the intricacies of student engagement, emphasizing the importance of active involvement in the learning process. This book opens with a reflection on education, where traditional practices give way to innovative pedagogies. This includes a new pedagogical approach that not only imparts knowledge but also cultivates socially responsible citizens. The book provides a rich tapestry of theoretical foundations, curriculum development strategies, and innovative pedagogical approaches that move beyond passive learning. From evaluating the impact of service learning to incorporating technology and measuring learning outcomes, each chapter offers theoretical frameworks, practical experiments, and real-life examples for educators, administrators, and policymakers. The book addresses the challenges and barriers to achieving meaningful student engagement, proposing practical solutions and recommendations. It emphasizes the role of service learning in building reciprocal relationships with communities and fostering inclusivity. Case studies and best practices from diverse educational settings showcase the effectiveness of different approaches to student engagement. The diverse audience within and beyond the education sector, including students, faculty members, parents, policymakers, NGOs, and community organizations, will find within the pages of this book valuable insights and tools to create more effective and meaningful learning experiences. The book covers a broad spectrum of topics, from the institutionalization of service learning to motivations for sustainable engagement, making it an indispensable resource for anyone passionate about shaping the future of education. 2024 by IGI Global. All rights reserved. -
Elementary Statistical Methods
This is the first book of two volumes covering the basics of statistical methods and analysis. Significant topics include concepts of research and data analysis, descriptive statistics, probability and distributions, correlation and regression, and statistical inference. The book includes useful examples and exercises as well as relevant case studies for proper implementation of the discussed tools. This book will be a valuable text for undergraduate students of statistics, management, economics, and psychology, wanting to gain basic understanding of statistics and the usage of its various concepts. The Editor(s) (if applicable) and The Author(s). under exclusive license to Springer Nature Singapore Pte Ltd. 2022. -
Emerging Issues and Trends in Indian Business and Management: Volume 2: Business and Society: Issues and Cases in the Indian Context
There are many theories on why managers do not (as a behavior) or should not (as a value) supplement profit orientation with people-centrism and planet sensitivity. In practice, managers do not supplement profit orientation with considerations for people and the planet unless they have the tools and know how to make that possible. This book seeks to address that by focusing on the normative dimension of organizational development. There are two competing norms for developing an organization: first, as a profit-oriented business enterprise; and second, as a people-centric, planet-sensitive, profit-oriented business or social enterprise. The performance of a business is a concern for all stakeholders. With the growing realization of the importance of indirect stakeholders like the society and the planet, it is increasingly important to raise awareness about the social and environmental responsibilities of businesses and organizations. This book is a must-read for academics, researchers, practitioners and policymakers who are concerned about the triple bottom-line (Planet-People-Profit) performance of businesses, which is critical for their long-term sustainability. It covers topics pertaining to the relationship between business and society, including social entrepreneurship and corporate social responsibility, among others, and draws from real-life case studies on social initiatives. 2024 by World Scientific Publishing Co. Pte. Ltd. -
Emerging Nanomaterials for Catalysis and Sensor Applications
This book reviews emerging nanomaterials in catalysis and sensors. The catalysis section covers the role of nano-photocatalysts in organic synthesis and health care application, oxidation and sulphoxidation reactions, liquid phase oxidation, hydrogen evolution and environmental remediation. It highlights the correlation of surface properties and catalytic activity of the mesoporous materials. The sensor section discusses the fabrication and development of various electrochemical, chemical, and biosensors. Features: Combines catalysis and sensor applications of nanomaterials, including detailed synthesis techniques of these materials. Explores methods of designing, engineering, and fabricating nanomaterials. Covers material efficiency, their detection limit for sensing different analytes and other properties of the materials. Discusses sustainability of nano materials in the industrial sector. Includes case studies to address the challenges faced by research and development sectors. This book is aimed at researchers and graduate students in Chemical Engineering, Nanochemistry, Water Treatment Engineering and Labs, Industries, Research Labs in Catalysis and Sensors, Environmental Engineering, and Process Engineering. 2023 selection and editorial matter, Anitha Varghese and Gurumurthy Hegde; individual chapters, the contributors. -
Enhancing curricula with service learning models
In today's digital age, technological advancements permeate every sector, especially higher education. However, higher education must go beyond merely integrating AI into the curriculum. Additionally, it needs to prioritize educating students about societal issues. Integrating service learning into higher education curriculums, however, is a significant challenge facing schools today. There is a need for comprehensive research on its effectiveness and guidance on institutionalizing it effectively. This hampers its potential to foster civic engagement and social responsibility among students. With clear strategies and best practices, institutions can implement service learning programs that benefit all stakeholders. Enhancing Curricula with Service Learning Models provides a comprehensive blend of theoretical frameworks, practical experimentation, and real-world examples to guide educators, administrators, and policymakers in fostering profound student engagement. It emphasizes the role of emerging educational paradigms, like service-learning, in instilling a sense of civic duty and purpose in students. By enriching the educational dialogue with an emphasis on the pivotal role of student engagement in creating transformative and purposeful learning experiences, this book empowers educators and institutions to create impactful and sustainable programs. To ensure that educators and stakeholders are equipped with the knowledge and tools necessary to cultivate environments that encourage active student participation, Enhancing Curricula with Service Learning Models provides practical guidance on building effective tri-party relationships between community partners, academia, and students. By offering a meta-analysis of service learning practices, this book is a valuable resource for institutions looking to enhance their academic quality and community engagement. 2024 by IGI Global. All rights reserved. -
Entrepreneurship Education: Experiments with Curriculum, Pedagogy and Target Groups
The book provides an overview of developments in the field of entrepreneurship education, with special reference to global perspectives on innovations and best practices, as well as research in the emerging economy context. It focuses on various experiments in curriculum design, review and reform in addition to the innovative processes adopted for developing new content for entrepreneurship courses, in many cases with an assessment of their impact on students' entrepreneurial performance. Further, it discusses the pedagogical methods introduced by teachers and trainers to enhance the effectiveness of students' learning and their development as future entrepreneurs. It explains the various initiatives generally undertaken to broaden the scope of entrepreneurship education by extending it beyond regular students and offering it to other groups such as professionals, technicians, artisans, war veterans, and the unemployed. The book is a valuable resource for researchers and academics working in the field of entrepreneurship education as well as for trainers, consultants, mentors and policy makers. Springer Nature Singapore Pte Ltd. 2017. All rights are reserved.